diff --git a/test/python/fx_importer/sparse_test.py b/test/python/fx_importer/sparse_test.py
index a3a98f0dd..23b573211 100644
--- a/test/python/fx_importer/sparse_test.py
+++ b/test/python/fx_importer/sparse_test.py
@@ -137,7 +137,7 @@ def export_and_import(f, *args, **kwargs):
 def sparse_jit(f, *args, **kwargs):
     """This method compiles and runs the given callable using linalg backend."""
     # Import module and lower into Linalg IR.
-    module = export_and_import(f, *args, *kwargs)
+    module = export_and_import(f, *args, **kwargs)
     run_pipeline_with_repro_report(
         module,
         (
@@ -152,12 +152,19 @@ def sparse_jit(f, *args, **kwargs):
     backend = RefBackendLinalgOnTensorsBackend()
     compiled = backend.compile(module)
     invoker = backend.load(compiled)
+    xargs = []
+    # Prepare the buffer parameters (assume all dense).
+    # TODO: filters out scalar arguments, anything else?
+    params = dict(f.named_buffers(remove_duplicate=True))
+    params_flat, params_spec = torch.utils._pytree.tree_flatten(params)
+    for p in params_flat:
+        if len(p.shape) > 0:
+          xargs.append(p.numpy())
     # Prepare input parameters. Sparse input tensors are split into
     # their composite tensors. All PyTorch tensors are converted
     # to their backing numpy arrays. Note that the output consists
     # of numpy arrays as well, which can trivially be reconstructed
     # into PyTorch tensors (dense and sparse).
-    xargs = []
     for a in args:
         if a.layout is torch.sparse_coo:
             # Construct the additional position array required by MLIR with data